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Fragmentation in robot learning systems limits their effectiveness, but a unified framework could enable robots to reason and act more reliably in complex environments.
Stochastic attention isn't just a regularizer; it fundamentally transforms how visual information is learned in VLMs, leading to more stable and reliable models.
